Identification of Potential Diagnoses Based on Immune Infiltration and Autophagy Characteristics in Major Depressive Disorder

Background: Major depressive disorder (MDD) is a serious mental illness characterized by mood changes and high suicide rates. However, no studies are available to support a blood test method for MDD diagnosis. The objective of this research was to identify potential peripheral blood biomarkers for MDD and characterize the novel pathophysiology. Methods: We accessed whole blood microarray sequencing data for MDD and control samples from public databases. Biological functions were analysed by GO and KEGG pathway enrichment analyses using the clusterprofile R package. Infiltrated immune cell (IIC) proportions were identified using the CIBERSORT algorithm. Clustering was performed using the ConsensusClusterPlus R package. Protein–protein interactions (PPI) were assessed by constructing a PPI network using STRING and visualized using Cytoscape software. Rats were exposed to chronic unpredictable mild stress (CUMS) for 6 weeks to induce stress behaviour. Stress behaviour was evaluated by open field experiments and forced swimming tests. Flow cytometry was used to analyse the proportion of CD8+ T cells. The expression of the corresponding key genes was detected by qRT–PCR. Results: We divided MDD patients into CD8H and CD8L clusters. The functional enrichment of marker genes in the CD8H cluster indicated that autophagy-related terms and pathways were significantly enriched. Furthermore, we obtained 110 autophagy-related marker genes (ARMGs) in the CD8H cluster through intersection analysis. GO and KEGG analyses further showed that these ARMGs may regulate a variety of autophagy processes and be involved in the onset and advancement of MDD. Finally, 10 key ARMGs were identified through PPI analysis: RAB1A, GNAI3, VAMP7, RAB33B, MYC, LAMP2, RAB11A, HIF1A, KIF5B, and PTEN. In the CUMS model, flow cytometric analysis confirmed the above findings. qRT–PCR revealed significant decreases in the mRNA levels of Gnai3, Rab33b, Lamp2, and Kif5b in the CUMS groups. Conclusion: In this study, MDD was divided into two subtypes. We combined immune infiltrating CD8+ T cells with autophagy-related genes and screened a total of 10 ARMG genes. In particular, RAB1A, GNAI3, RAB33B, LAMP2, and KIF5B were first reported in MDD. These genes may offer new hope for the clinical diagnosis of MDD.

INTRODUCTION
Major depressive disorder (MDD) is the leading cause of the burden of mental health-related disease, affecting approximately 300 million people worldwide (Herrman et al., 2019). Currently, MDD is mainly diagnosed and treated on the basis of different symptoms and signs fitting to rigorous diagnostic categories, such as the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) (Hasin et al., 2018). However, this diagnostic method has a certain degree of subjectivity, resulting in considerable limitations and errors in the diagnosis results. In the last several decades, some diagnostic biomarkers for MDD have been identified. However, none of these biomarkers achieve satisfactory specificity and sensitivity for clinical application (Kennis et al., 2020). One of the main causes of this dilemma is the lack of a thorough understanding of the pathophysiology and pathogenesis of depression, which complicates the diagnosis and treatment of depression.
Clinical and preclinical studies have suggested that the immune system is implicated in the pathophysiology of MDD (Dantzer et al., 2008;Wohleb et al., 2016). Changes in peripheral and central immune function are found in MDD patients (Medina-Rodriguez et al., 2018). The reduced circulating T cells and regulatory B cells indicate that the adaptive immune system malfunctions in MDD patients (Ahmetspahic et al., 2018). In addition, the ability of T cells in MDD patients to respond to stimuli is decreased, suggesting that T cells have an immunosuppressive phenotype in MDD (Herbert and Cohen, 1993;Zorrilla et al., 2001;Irwin and Miller, 2007). The above results have motivated a search for biomarkers of immune status that can be used to stratify MDD cases. Blood is an easily obtainable tissue used to analyse immune biomarkers, while collecting brain tissue is inconvenient. Among the blood biomarkers associated with MDD immune pathogenesis, the central nervous system (CNS) immune status has been identified to be related to or result from peripheral immune status. Furthermore, gene transcription detected within human blood samples has been increasingly suggested to be related to the transcript levels detected in numerous additional body systems, such as the CNS (Wittenberg et al., 2020). Therefore, the development of immune biomarkers and accompanying diagnoses could be used to guide new therapeutic methods.
Previous studies have demonstrated that autophagy is associated with the immune response, cytokine production and secretion, and inflammasome activation in many physiological and pathological processes (Ali et al., 2020;Lee et al., 2020). Autophagy is a self-degradation process that maintains cellular homeostasis by autophagosome formation (Onorati et al., 2018). Widely used antidepressants such as amitriptyline and fluoxetine activate autophagy in hippocampal neurons (Gulbins et al., 2018). Autophagy marker levels are increased after treating cells with antidepressants (Gassen et al., 2015). Although the function of autophagy in MDD has been reported, the intersection between immune infiltration and autophagy affecting the MDD process is unknown.
In this study, we studied immune status biomarkers that could be used for MDD stratification. Briefly, we identified IIC-based MDD subtypes by CIBERSORT and consensus clustering using the GEO database. Marker genes of the subtypes were also obtained and functionally enriched to reveal potential functions. In summary, this study identified new subtypes of depression that exceed the current diagnostic limits, which may help identify individuals who are most likely to benefit from targeted therapies.

Data Collection
Two gene expression profiles of whole-blood samples from MDD patients and healthy donors were obtained from the Gene Expression Omnibus (GEO) database. The GSE98793 dataset included 128 MDD patients and 64 healthy controls. Twenty-one MDD samples, eight bipolar disorder samples (BDI), and 24 healthy donors were included in the GSE39653 dataset. Eight bipolar disorder samples were excluded from this study. The GSE98793 dataset was used as a training set, and the GSE39653 dataset was used as a testing set. In addition, a total of 222 autophagy-related genes (ARGs) were obtained from the Human Autophagy Database (HADb).

Elimination of the Batch Effect
Batch effects are subgroups of measurements with different qualitative behaviours under different conditions independent of the biological or scientific variables under study (Leek et al., 2010). In this study, the GSE98793 dataset used as the training set was composed of two batches of sequencing data. Therefore, we performed a batch effect reduction exercise on the two batches of sequencing data in this dataset using the removeBatchEffect function of the limma package in R. Principal component analysis (PCA) was used to demonstrate the distribution of samples before and after the removal of the batch effect (Supplementary Figure S1). Moreover, to maximally increase the reliability of the validation results in the GSE39653 dataset, we narrowed the batch effect between the GSE39653 dataset and the GSE98793 dataset using the same method (Supplementary Figure S2). an estimation of the abundances of member cell types in a mixed cell population, using gene expression data) precisely quantified the different types of immune cells in each sample (Newman et al., 2015). In the current study, LM22 (the original CIBERSORT gene signature file) was utilized to measure the fractions of immune cells from MDD and healthy samples. Samples with a p-value less than 0.05 suggested that the fractions inferred by the algorithm are accurate (Ali et al., 2016) and could be used for further analysis. In the present study, all samples from the GSE98793 (128 MDD and 64 healthy samples) and GSE39653 (21 MDD samples and 24 healthy samples) datasets were satisfied with the CIBERSORT-P < 0.05 criterion. The sum of the immune cell type fractions assessed for each sample was 1, which could be regarded as cell fractions compared between immune cell types and datasets. Based on the IIC abundance profiles in the GSE98793 and GSE39653 datasets, a Wilcoxon rank-sum test was performed to analyse the comparison of IIC proportions between the healthy and MDD groups. Next, the correlation between IICs was analysed by Pearson correlation analysis. In addition, immune cytolytic activity was another criterion used to measure immune infiltration following the geometric mean of granzyme A and perforin 1 (Connor et al., 2017).

Unsupervised Consensus Clustering
Based on the fraction profile of IICs obtained by CIBERSORT, unsupervised consensus clustering was performed using the R package ConsensusClusterPlus (Wilkerson and Hayes, 2010) on MDD samples in the training set (n = 128) and validation set (n = 21) to identify MDD immune subtypes. Notably, 128 MDD samples in the training set were from the GSE98793 dataset; 21 MDD samples in the validation set were taken from the GSE39653 dataset. All MDD samples passed the CIBERSORT-P < 0.05 test. The K-means algorithm was used for 1,000 resampling iterations to ensure the stability of the consensus clustering. The optimal cluster number was determined using the consensus matrix (CM) and CDF curves of the consensus score (Zhou et al., 2020).

Identification of Marker Genes in Different Clusters
The Limma R package (version 3.42.2) based on t-test statistical significance was applied to identify the marker genes, which were defined as significant by an adjusted p-value less than 0.05 (Tusher et al., 2001). The gene expression level acted as the primary variable for comparison between three clusters in the GSE98793 dataset. Here, marker genes of subgroups refer to genes that were overexpressed or repressed in the target subgroup compared to other subgroups, For example, marker genes for the T cell CD8H subpopulation were defined as genes that were significantly upand down-regulated in the T cell CD8H subpopulation compared to the T cell CD8L + control group (adj. p < 0.05).

Enrichment Analysis
Annotations and classifications of genes were carried out by GO (Zhang et al., 2017) according to biological process (BP), molecular function (MF), and cellular component (CC) (Ashburner et al., 2000;Consortium, 2006). KEGG interpreted molecular interactions, reactions and relationship networks (Kanehisa and Goto, 2000). Herein, the clusterPrifiler R package was applied for GO enrichment and KEGG pathway analyses of the identified marker genes and ARMGs with a threshold p-value < 0.05.

Formation of Protein-Protein Interaction Network
STRING (http://string-db.org/) (von Mering et al., 2003), a tool for interacting gene retrieval, is a biological database for predicting PPI information. The marker genes were mapped to STRING for interaction evaluation, and a confidence level >0.9 was considered significant. Subsequently, we selected nodes with a degree ≥10 to construct a subnetwork and identified marker genes belonging to ARGs. Then, Cytoscape (Shannon et al., 2003) was utilized to construct PPI networks.

Identification of Key Autophagy-Related Marker Genes
Overlap analysis was performed to identify common elements between marker genes of T cell CD8H and 222 ARGs, which were defined as ARMGs. the overlap analysis was performed by the Jvenn online analysis tool (http://jvenn.toulouse.inra.fr/app/ example.html). Briefly, the list of T cell CD8H-marker genes and 222 ARGs was uploaded to the Jvenn tool to identify the overlapping elements in the two gene lists. Subsequently, the same method was used to identify ARMGs in the list of T cell CD8H-marker genes with degree ≥10, which were considered as key ARMGs.

Animals
The Animal Care and Use Committee of Dalian Medical University (Dalian, China) authorized the animal experiments in this study, which were performed in accordance with the National Institute of Health Guide. The Experimental Animal Centre of Dalian Medical University provided Sprague-Dawley rats (200-250 g), which were housed in a specialized pathogenfree facility. Rats were maintained under a stringent 12-h light and 12-h dark cycle and had free access to water and food. All experimental protocols were approved by Animal Experiment Ethics Committee of Dalian Medical University (Reference number: AEE19087).

Chronic Unpredictable Mild Stress Procedure
Chronic unpredictable stress stimulation was used to establish the chronic stress model. According to our previous research, we adopted an improved stress stimulation method . All animals were allowed to adapt to the living environment for 1 week. During this experiment, the rats were placed in a cage in a separate room without interference. Animals received one of the following stressors once a day for 6 weeks: restraint for 1 h, cold water swimming (4°C) for 5 min, warm water swimming (45°C) for 5 min, food or water deprivation for 24 h, tail clamp for 1 min, overnight illumination for 12 h, damp bedding (200 ml water in 100 g sawdust bedding) for 24 h, and cage tilting (45°C) for 24 h. To prevent habituation and provide an unpredictable characteristic for stressors, all the stressors were randomly applied within 1 week and repeated throughout the 6week experiment. At the same time, animals in the control group maintained a normal feeding schedule.

Open Field Test
The rats' behaviour in the open field was examined using the Super Maze V2.0 Animal Behaviour Video Analysis System (Shanghai Xinruan Technology Co., Ltd., China). Rats were recorded for 5 min. The box (50 cm × 50 cm × 40 cm) included black inner walls with a bottom containing a 25square lattice for analysis. The light was set at approximately 40 LX, while the laboratory noise in the background was set <65 dB. The total distance travelled and the number of crossing in central area of the open field were analysed. The total motor distance reflected the general locomotor activity of rats. Exploratory behavior and anxiety were measured by the number of crossing in central area. The cages were cleaned with 75% alcohol after the test to avoid odour affecting tests with subsequent animals.

Forced Swimming Test
The FST was used to assess motivated behavior. The FST was performed according to previously reported methods . Briefly, a rat was placed into a transparent cylinder filled with water (25°C). The rats were considered immobile as long as they remained passively floating in the water with their noses out of the water. All rats were forced to swim for 6 min: the first 2 min were the adaptation stage, and the duration of immobility was recorded during the last 4 min.

Flow Cytometric Analysis
The peripheral blood samples from all the groups at 6 weeks were collected in EDTA tubes (BD Vacutainer, United States). The samples were blocked with fuorescence conjugated antibodies [anti-mouse CD3 antibody and anti-mouse CD8 antibody (BD Biosciences, United States)] for 20 min in the dark, at room temperature. After washing, the samples were lysed using the lysing solution for 10 min and washed and suspended in PBS. A minimum of 10,000 stained nucleated cells per tube were acquired using a flow cytometer (BD Biosciences, United States) and analyzed using FlowJo 7.6 software (TreeStar, Inc.).

Quantitative Real-Time Polymerase Chain Reaction
All whole-blood samples lysed with TRIzol LS reagent (Invitrogen, United States), and total RNA was isolated following the manufacturer's instructions. Then the concentration and purity of the RNA solution were quantified using a NanoDrop 2000FC-3100 nucleic acid protein quantifier (Thermo Fisher Scientific, United States). The extracted RNA was reverse-transcribed to cDNA using the TranScript Top Green qPCR kit (TransGen Biotech, China) prior to qRT-PCR. The qRT-PCR reaction consisted of 2 µl of reverse transcription product, 10 µl of 2 × SYBR Qpcr Master Mix (Bori, China), and 0.4 µl each of forward and reverse primer. PCR was performed in a BIO-RAD CFX96 Touch TM PCR detection system (Bio-Rad Laboratories, Inc., United States) under the following conditions: initial denaturation at 95°C for 30 s, followed by 40 cycles that each involved incubation at 95°C for 5 s, 60°C for 30 s, followed by 65°C for 5 s, and 95°C for 5 s. The forward primer of Rab1a was "TCCTCCCCTTCCTTT ACCCG". The reverse primer of Rab1a was "CCGTATACGTGT CATCCGCAA". The forward primer of Gnai3 was "TGAGGA CGAGGAAATGAACCGA". The reverse primer of Gnai3 was "TGCACGTTTTTGGTGTCAGTG". The forward primer of Rab33b was "ATGGAGAACGCATTAAGATCCAGT". The reverse primer of Rab33b was "AAGCTGGCCATGTTGGTC AT". The forward primer of Lamp2 was "GGCTAATGGCTC AGCTTTCCA". The reverse primer of Lamp2 was "TGATGG CGCTTGAGACCAAT". The forward primer of Kif5b was "ACG AGTCTGAAGTGAACCGC". The reverse primer of Kif5b was "ATGCATAAGGCTTGGACGCA". The forward primer of Gapdh was "ATGCCGCCTGGAGAAACC". The reverse primer of Gapdh was "GCATCAAAGGTGGAAGAATGG". All primers were synthesized by Sangon biotech (Sangon biotech, China). The GAPDH gene served as an internal control, and the relative expression of five genes was determined using the 2 −Δ (ΔCT) method. Statistical differences of Rab1a, Gnai3, Rab33b, Lamp2, Kif5b genes between control and CUMS samples were detected by unpaired t-tests, using GraphPad Prism 7 (GraphPad Software, United States), and the level of statistical significance was tested and represented as * for p < 0.05.

Statistical Analysis
R software (version 3.6.0) was used for statistical analyses. The Wilcoxon rank-sum test assessed the different ARMGs in the CD8 + T cell cluster and other clusters. SPSS 20.0 (IBM, NY) and GraphPad Prism 7 (GraphPad, CA) software were used for statistical analysis. The data are presented as the mean ± SEM. One-way ANOVA and Tukey's multiple comparison post hoc tests were conducted to determine differences between individual groups. Student's t-test was employed to identify differences between two groups. A value of p < 0.05 was considered statistically significant.

Analysis of Immune Landscape in the GSE98793 Dataset
CIBERSORT (p < 0.05) was used to calculate the differential IICs between 128 MDD samples and 64 controls following the Wilcoxon rank-sum test from GSE98793 ( Figures 1A,B, Supplementary Table S1). The number of resting NK cells (p < 0.01), monocytes (p < 0.01) and M0 macrophages (p < 0.01) was increased in MDD patients, while the other immune cell types, including CD8 + T cells (p < 0.05) and gamma delta T cells (p < 0.001), were decreased ( Figures 1A,B, Supplementary Table S1). These results indicated different IIC proportions in MDD samples and controls from the GSE98793 dataset.

Identification and Validation of Major Depressive Disorder Subtypes Based on Infiltrated Immune Cell
To identify subgroups of samples, immune infiltration MDD samples were selected based on a consensus clustering analysis using the Consensus Cluster Plus package. Based on the CIBERSORT algorithm, 128 MDD samples with p < 0.05 were mined from the GSE98793 cohort and analysed by consensus clustering using the R package Consensus Cluster Plus. The results revealed that the relative change in the area under the cumulative distribution function (CDF) curve was significantly reduced when k = 3, which implied that the sample clusters were stable and robust (Figures 2A-C). Among them, Cluster one Similarly, the cluster dendrogram also exhibited three individualized clusters among all MDD samples ( Figure 2D).
The heatmap showed that CD8 + T cells and neutrophils exhibited significantly different proportions among the three clusters, including a high proportion of CD8 or neutrophils, an intermediate proportion of CD8 or neutrophils, and a low proportion of CD8 or neutrophil clusters ( Figure 2E), indicating differentially expressed IICs between MDD samples, especially CD8 + T cells and neutrophils.
Next, the GSE39653 dataset was used to validate the clustering in Figure 2. The fractions of IICs among 45 samples (24 controls and 21 MDD samples) from the GSE39653 dataset were evaluated, and the results showed that B cell memory (p < 0.01) and CD8 + T cells (p < 0.05) were significantly lower in MDD samples than in the control group; naive B cells (p < 0.05) were significantly overexpressed in MDD samples (Supplementary Figure S4, Supplementary Table S2). The consensus clustering results revealed that MDD samples were divided into two clusters ( Figures 3A-D). Among them, Cluster one contained 15 MDD samples and Cluster two included six MDD samples. All samples were divided into high and low proportions of CD8 + T cells based on IIC fractions ( Figure 3E).
Based on a previous study (Figure 2), MDD was finally divided into two subtypes-a high proportion of CD8 + T cells (T cells CD8H; n = 12) and a low proportion of CD8 + T cells (T cells CD8L; n = 116)-for further research.

Identification of Marker Genes in the T Cell CD8H Cluster and T Cell CD8L Cluster
To explore the differences between subclusters and identify the biomarkers of each cluster, the marker genes were determined using the Limma R package. A total of 6,159 marker genes, including 3,728 upregulated and 2,431 downregulated genes, were screened in the T cell CD8H cluster (compared to the T cell CD8L + control group). The top five genes with the largest fold change were CTB-167B5.2, TMEM55A, RP11-488L18.10, OLFM4, and RAB33B ( Figure 4A; Table 1). Moreover, 2,423 marker genes, including 1,059 upregulated and 1,346 downregulated marker genes, were identified in the T cell CD8L cluster (compared to the T cell CD8H + control group), including LCN2, CNTNAP3B, DEFA4, CEACAM8, and OLFM4, which were the five genes with the largest fold change ( Figure 4A; Table 1). One hundred thirty-six marker genes (56 upregulated genes and 80 downregulated genes) were identified in the normal group (compared to the T cell CD8H + T cell CD8L group, Figure 4A), but this study did not focus on these genes.

Functional Enrichment Analysis of Marker Genes in the T Cell CD8H Cluster and T Cell CD8L Cluster
All of the marker genes, as previously described, were analysed for their potential function in the Gene Ontology resource (GO; http:// geneontology.org) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. GO terms in different GO categories (BP, MF, and CC) showed that T cell CD8L marker genes were mainly related to immune cell activation (neutrophil and T cell activation) ( Figure 4B). Meanwhile, the marker gene CD8H of T cells was significantly associated with immune (immune response-regulating cell surface receptor signalling pathway), macroautophagy, and posttranscriptional modification processes (ribonucleoprotein complex biogenesis) ( Figure 4C). Furthermore, KEGG pathway analysis found that T cell CD8H marker genes were mainly involved in the T cell receptor signalling pathway, endoplasmic reticulum protein processing, and endocytosis ( Figure 4D), while T cell CD8L marker genes were enriched in Th17 cell differentiation and the T cell receptor signalling pathway ( Figure 4E). Notably, we found that the marker genes of the T cell CD8H cluster were enriched in terms related to autophagy, such as "macroautophagy". Therefore, we focused on the T cell CD8H cluster, which was enriched in many autophagy-related terms.

Analysis of Autophagy-Related Marker Genes in the T Cells CD8H Cluster
To understand the possible role of ARGs in the T cell CD8H clusters in MDD patients, we performed the following analyses. First, 222 ARGs integrated from the HADb database intersected with 6,159 marker genes identified in the T cell CD8H cluster ( Figure 5A). The Venn diagrams and volcano plots in Figures 5A,B show 47 upregulated and 63 downregulated ARMGs (p < 0.05). Then, the heatmap displayed the expression pattern of the ARMGs between T cells CD8H and other clusters (Supplementary Figure  S5A). The PCA based on the different clusters revealed a distinguishing outcome, which is shown in Supplementary  Figure S5B. These findings showed that 110 of 222 ARGMs in the HADb database were differentially expressed in T cell CD8H subtypes, suggesting that autophagy-related genes might play a role in T cell CD8H subtypes.
Functional Enrichment Analysis of Autophagy-Related Marker Genes in the T Cell CD8H Cluster GO enrichment analysis was performed following the 110 ARMGs, including the BP, CC, and MF categories. The results indicated that macroautophagy, processes utilizing autophagic mechanisms, and autophagosome assembly were enriched terms in the BP category. In the CC category, 110 ARMGs were considerably enriched in terms of the autophagosome, phagophore assembly site, and membrane region. In the MF category, those ARMGs had obvious enrichment in terms of heat shock protein binding, cysteine-type endopeptidase activity, and ubiquitin-like protein ligase binding ( Figure 6A, Supplementary Table S3).
KEGG pathway analysis suggested that ARMGs had visible enrichment in "Autophagy-animal" and "Autophagy-other". In addition, the "Apoptosis" pathway was enriched in ARMGs, which was potentially implicated in the development and progression of MDD ( Figure 6B, Supplementary Table S4).

Screening of the Key Autophagy-Related Marker Genes
To investigate the interactions between 6,159 marker genes of the T cell CD8H cluster, we obtained a PPI network containing 566 nodes and 2,299 edges after presenting discrete individual proteins in STRING with an interaction score >0.9 (Supplementary Figure S6A). Among them, UBE2D1 (degree = 52), PIK3CA (degree = 41), and EP300 (degree = 40) were the top three proteins in terms of degree value, indicating that these proteins had tight interactions with numerous other marker gene proteins. We screened 170 marker genes with a degree ≥10 to further identify important ARGs and visualized a PPI subnetwork containing 1,363 interactions by Cytoscape (Supplementary Figure S6B). We found that RAB1A (degree = 30), RAB11A (degree = 26), VAMP7 (degree = 17), GNAI3 (degree = 16), PTEN (degree = 14), MYC (degree = 13), HIF1A (degree = 12), RAB33B (degree = 11), KIF5B (degree = 11), and LAMP2 (degree = 10) were ARGs, which were considered key ARMGs ( Table 2). (A) GO enrichment result of ARMGs. The x-axis label represents fold enrichment and y-axis label represents GO terms. (B) Top 10 significantly enriched KEGG pathway in ARMGs. Y-axis label represents pathway, and X-axis label represents fold enrichment. Size and color of the bubble represent amount of ARMGs enriched in term and enrichment significance, respectively. The larger the circle, the more genes it contained; conversely, the smaller the circle, the fewer genes it contained. The color of the circle is correlated with the p Value. The lower the p Value is, the closer it is to the red value. The higher the p Value is, the closer it is to the green value. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; ARMGs, autophagy-related marker genes. "BP" stands for "biological process", "CC" stands for "cellular component" and "MF" stands for "molecular function".
Frontiers in Genetics | www.frontiersin.org April 2022 | Volume 13 | Article 702366 The Proportion of CD8 + T Cells and the Level of Corresponding Key Genes in the Peripheral Blood of Rats in the Chronic

Unpredictable Mild Stress Model
To confirm the success of the chronic stress model, we first evaluated stress response by open field experiments and forced swimming tests. The results showed that rats exhibited stress response ( Figures 7A-D). Furthermore, we detected the proportion of CD8 + T cells using flow cytometry. The results showed that the proportion of CD8 + T cells was reduced in the CUMS groups compared with the control groups ( Figures 7E,F).
In addition, we selected five key ARMGs reported in MDD for the first time from 10 key ARMGs for verification. qRT-PCR revealed significant decreases in the mRNA levels of Gnai3, Rab33b, Lamp2, and Kif5b in the CUMS groups compared to those in the related control groups (Supplementary Figure  S7A-D).

DISCUSSION
In recent decades, evidence of immune imbalance in MDD has been increasing (Leonard, 2010). For example, compared with the healthy group, the immune cell counts in the whole blood of patients with MDD were increased, especially CD4 + T cells, neutrophils, and monocytes (Pfau et al., 2017). To further discuss the role of immune cell infiltration in MDD, we comprehensively evaluated immune infiltration by using CIBERSORT in MDD. In this study, we discovered different IIC proportions between peripheral blood from patients with MDD and that from the healthy group. Our results found that the proportions of resting NK cells, monocytes, and macrophages in patients with MDD were increased compared with those in the healthy group. The proportion of gamma delta T cells in patients with MDD was reduced compared with that in the healthy group. According to previous research reports, monocytes and macrophages are increased in MDD patients (Kronfol, 2002;Hughes et al., 2021), which is consistent with our results. Immune infiltration of resting gamma delta T cells and resting NK cells in MDD has not been reported. Importantly, we found in GSE98793 dataset and GSE39653 dataset that the proportion of CD8 + T cells in peripheral blood of patients with MDD was lower than that of healthy people. We obtained the same results in the chronic stress rats model. CD8 + T cells play a major role in immune regulation. Recent data have demonstrated that reducing CD8 T lymphocyte apoptosis can regulate the immune microenvironment in depressed mice (Lu et al., 2017). Additionally, bipolar disorder is highly correlated with a reduction in circulating CD8 + T cell subpopulations (Magioncalda et al., 2018). Interestingly, although the proportion of CD8 + T cells was reduced in MDD patients, it was also different between patients with MDD. We found that MDD was divided into two subtypes-a high proportion of CD8 + T cells (T cells CD8H) and a low proportion of CD8 + T cells (T cells CD8L)-by further analysis, suggesting that the proportion of CD8 + T cells may be an essential factor affecting the development of depression. However, why some Rab1a is the small G protein that regulates vesicle transport from endoplasmic reticulum to and through Golgi. Rab1a overexpression can increase the expression level of TLR4 Song et al. (2020). The association between RAB1A and MDD has not been reported GNAI3 G Protein Subunit Alpha I3 Signaling is mediated via effector proteins, such as adenylate cyclase. Inhibits adenylate cyclase activity, leading to decreased intracellular cAMP levels Kimple et al. (2009). The association between GNAI3 and MDD has not been reported VAMP7 Vesicle Associated Membrane Protein 7 Involved in the targeting and/or fusion of transport vesicles to their target membrane during transport of proteins from the early endosome to the lysosome. Vamp7 might provide insight into treatment of MDD Li et al. (2019)  RAB33B Ras-Related Protein Rab-33B Protein transport. Acts, in coordination with RAB6A, to regulate intra-Golgi retrograde trafficking. It is involved in autophagy, acting as a modulator of autophagosome formation. The association between RAB33B and MDD has not been reported MYC MYC Proto-Oncogene MYC is somehow involved in depressed suicide Zeng et al. (2020) LAMP2 Lysosomal Associated Membrane Protein 2 Plays an important role in chaperone-mediated autophagy, a process that mediates lysosomal degradation of proteins in response to various stresses and as part of the normal turnover of proteins with a long biological halflive. The association between LAMP2 and MDD has not been reported RAB11A Ras-Related Protein Rab-11A Antidepressants AMPH leads to an increase of NET (The norepinephrine transporter is a major target for medications used for the treatment of depression) in a Rab11-dependent manner Matthies et al. (2010) HIF1A Hypoxia Inducible Factor 1 Subunit Alpha altered expression of HIF-1 and its target genes mRNA in peripheral blood cells are associated-mainly in a statedependent manner-with mood disorders (especially with MDD). In addition, altered expression of HIF-1 and its target genes may be associated with the pathophysiology of depression Shibata et al. (2013)  KIF5B Kinesin Family Member 5B Syntabulin acts as a KIF5B motor adaptor and mediates anterograde transport of presynaptic cargoes and mitochondria, presynaptic assembly, and activity-induced plasticity Ma et al. (2009 MDD samples had high CD8 + T cell infiltration while others did not is unclear. We further studied which biological function was related to the proportion of CD8 T cells in MDD patients. Through functional enrichment analysis of the marker genes in each cluster, we found that the marker genes of the T cell CD8H cluster were enriched in terms related to autophagy. Autophagy can regulate immune system components, including natural killer (NK) cells, macrophages, dendritic cells (DCs), and T and B lymphocytes (Gerada and Ryan, 2020). In the process of macroautophagy, the autophagic vesicles that wrap abnormal proteins in the cytoplasm merge with lysosomes for degradation (Nishida et al., 2009). Interestingly, we found that the marker genes in the T cell CD8H cluster were significantly enriched in proteasome-mediated ubiquitin-dependent protein catabolic process, lysosomal membrane, ubiquitin-protein transferase activity, and ubiquitin-like protein transferase activity. Coincidentally, autopsies of MDD patients showed that synaptic protein synthesis in the prefrontal cortex was inhibited and mTOR phosphorylation levels were reduced, suggesting that autophagy was activated (Jernigan et al., 2011). Compelling evidence has revealed that autophagy is activated in MDD patients and that antidepressants exert antidepressant effects via autophagy regulation through different signalling pathways, suggesting that autophagy might be involved in the occurrence and development of depression (Shelton et al., 2011;Abelaira et al., 2014). According to some articles, markers for cellular autophagy are upregulated after antidepressant treatment (Zschocke and Rein, 2011). Additionally, the expression of markers for autophagy, such as beclin1, in the mouse brain increases after antidepressant treatment (Gassen et al., 2014). The above results indicate that autophagy pathways have vital effects on MDD. Interestingly, some articles suggest that mTOR (the major regulator of autophagy) is associated with memory CD8 + T cell differentiation. Based on these findings, rapamycin-mediated mTOR inhibition enhances memory CD8 + T cell magnitude and quality (Araki et al., 2009;Pearce et al., 2009). Therefore, autophagy plays an important role in the survival of effector CD8 + T cells (Xu et al., 2014). Our research confirmed that autophagy was highly correlated with the T cell CD8H cluster, suggesting that autophagy may be an important biological process that regulates the proportion of CD8 + T cells in MDD.
To date, although ARMGs have been widely reported as diagnostic and/or prognostic markers for many types of tumours, such as hepatocellular carcinoma (Huo et al., 2020), gastric cancer (Zhou et al., 2021), and ovarian cancer (Chen et al.), research on whether autophagy genes or proteins can serve as biomarkers in psychiatric disorders is limited. Recently, ARMGs have been reported to potentially be helpful for the diagnosis of schizophrenia . However, the diagnostic performance of ARMGs for MDD has not yet been explored. We established a PPI network to investigate the correlations among ARMGs, which revealed 10 hub genes, namely, RAB1A, GNAI3, VAMP7, RAB33B, MYC, LAMP2, RAB11A, HIF1A, KIF5B, and PTEN. The subnetwork of VAMP7, MYC, RAB11A, HIF1A, and PTEN screened out from our study has been shown to play a role in MDD ( Table 2). In particular, we discovered for the first time that RAB1A, GNAI3, RAB33B, LAMP2, and KIF5B might be potential markers for the diagnosis of depression. Rab1a controls the initiation of autophagy by regulating the trafficking of the ULK1 autophagy initiation complex to the phagophore (Webster et al., 2016). Its overexpression can increase the expression level of TLR4 . GNAI3 has been recognized as a regulator of autophagy (Vural et al., 2013); it is abundantly expressed in immune cells and regulates cytokine responses to bacterial products (Li et al., 2019). Defective mitophagy is driven by dysregulation of KIF5B, which induces an NLRP3-dependent proinflammatory response (Yang et al., 2014). Notably, Kif5b deficiency within haematopoietic cells plays an important role in the anticancer response mediated by effective CD8 + T cells (Belabed et al., 2020). This evidence suggests that Rab1a, GNAI3, and KIF5B can regulate both autophagy and inflammation. At the same time, these autophagy-related genes may be markers explaining why some MDD patients have a high proportion of CD8 T cells. Whether these genes can change the proportion of CD8 T cells and exert antidepressant effects remains to be further experimentally verified.

LIMITATION
This study had limitations. First, we identified several genes from microarray data analysis and verified their differential expression in the CUMS model. However, we did not further verify the functions of these selected genes. Therefore, we need a large number of clinical samples to verify our findings and clarify the potential mechanisms of how these genes affect the pathological stage.

CONCLUSION
MDD patients may be clustered according to the proportion of CD8 + T cells, which corresponds to the diagnosis for a certain type of patient. After clustering, the marker genes of the T cell CD8H cluster were found to be significantly related to autophagy, suggesting that autophagy may affect the immune microenvironment of MDD patients. We also screened five hub autophagy-related genes, which may offer new hope for the treatment and diagnosis of MDD patients in the future, although further study is required.

DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material.

ETHICS STATEMENT
The animal study was reviewed and approved by The Animal Care and Use Committee of the Dalian Medical University.


Xenobiotic-induced ribosomal stress compromises dysbiotic gut barrier aging: A one health perspective

Upon exposure to internal or environmental insults, ribosomes stand sentinel. In particular, stress-driven dysregulation of ribosomal homeostasis is a potent trigger of adverse outcomes in mammalians. The present study assessed whether the ribosomal insult affects the aging process via the regulation of sentinel organs such as the gut. Analyses of the human aging dataset demonstrated that elevated features of ribosomal stress are inversely linked to barrier maintenance biomarkers during the aging process. Ribosome-insulted worms displayed reduced lifespan, which was associated with the disruption of gut barriers. Mechanistically, ribosomal stress-activated Sek-1/p38 signaling, a central platform of ribosomal stress responses, counteracted the gut barrier deterioration through the maintenance of the gut barrier, which was consistent with the results in a murine insult model. However, since the gut-protective p38 signaling was attenuated with aging, the ribosomal stress-induced distress was exacerbated in the gut epithelia and mucosa of the aged animals, subsequently leading to increased bacterial exposure. Moreover, the bacterial community-based evaluation predicted concomitant increases in the abundance of mucosal sugar utilizers and mucin metabolic enzymes in response to ribosomal insult in the aged host. All of the present evidence on ribosomal insulting against the gut barrier integrity from worms to mammals provides new insights into organelle-associated translational modulation of biological longevity in a one health perspective.

Introduction
In response to internal or external stresses, cellular ribosomes stand sentinels. Stress-driven dysregulation of ribosomal integrity triggers eukaryotic translation initiation factor 2 subunit α (eIF2α)-mediated global translational inhibition. In spite of the global translational arrest, the ribosome-inactivating stress (ribosomal stress) triggers the expression of genes important for cellular homeostasis, as well as those integral to a variety of pathogenic processes involved in cell death, inflammation, and other stress responses [1][2][3]. In particular, humans are inevitably exposed to a specific ribotoxic (28S rRNA-damaging) stressor, deoxynivalenol (DON) which is the most frequently occurring mycotoxin in the global food and environmental systems [4][5][6][7][8]. In addition to DON, other trichothecene mycotoxins, antibiotic anisomycin, and ribosome-inactivating proteins (including ricin, α-sarcin, restrictocin, and shiga toxins) interfere with peptidyl transferase activity by directly or indirectly modifying 28s rRNA at one site in the α-sarcin/ricin loop (A4256) or two other sites (A3560 and A4045) in the peptidyl transferase center [9][10][11]. This modification leads to peptidyl transferase dysfunction and subsequent global translational arrest [12][13][14][15]. Mechanistically, damages in the structure and functionality of 28S ribosomal RNA during gene translation lead to homeostatic or pathogenic cellular reprogramming via intracellular sentinel signaling pathways, which include mitogen-activated protein kinase (MAPK)-linked signaling cascades as the primary platform in mammalian cells [1,16,17]. The 28S rRNA damage facilitates ribosomal binding to dsRNA-binding domains of protein kinase R (PKR) and induces enzymatic activation [18]. Following exposure to high levels of ribosomal stressors, activated PKR modulates stress-induced cell death processes via activation of MAPKs such as c-Jun N-terminal kinase 1 and 2 [19]. In contrast, low levels of exposure to the ribosomal insults may promote the expression of adaptive stress-responsive genes including pro-inflammatory genes via PKR-mediated ribosomal recruitment and activation of MAPKs such as p38 16,18 . The ribosomal 40S subunit serves as a scaffold for PKR under the ribosomal stress, which facilitates p38 mobilization and activation for target gene induction despite the global translational arrest. Such stress-sentinel pathways by ribosomal inactivation are potent etiological factors involved in epithelial inflammatory or malignant distress including inflammatory bowel diseases (IBD) and colorectal cancer in the experimental models [20][21][22][23][24][25]. Moreover, epidemiological studies suggest potent associations between ribosome-inactivating stress and human gastrointestinal illness [17,[26][27][28].
Gastrointestinal epithelium is a sentinel line against pathogens and participates in the maintenance of host integrity and is imprinted with accumulated environmental and dietary insults which can damage the gut and other organs via circulation. Disruption of gut epithelial barrier integrity can allow increased translocation of noxious microbes and substances across the intestinal barrier, leading to a greater risk of systemic infection, inflammation, and sepsis. The aging process can alter the integrity of the gut barrier and increase vulnerability to GI disorders. In particular, disorders of the gastrointestinal (GI) tract are particularly common in the elderly population, which may contribute to the overall vulnerability to sepsis-linked mortality in hosts with poor defenses [29]. Moreover, the epithelium-based defense in invertebrates, such as Caenorhabditis elegans, is important for the generation of evolutionarily conserved aspects of innate immunity, and the maintenance of epithelial integrity is associated with the lifespan of these organisms [30][31][32][33]. C. elegans are particularly dependent on the gut epithelial layer because they lack specialized leukocytes, such as macrophages and lymphocytes, for defending against pathogens [30,34]. This reduced complexity can be experimentally advantageous for the detailed characterization of innate immune signaling cascades. The present study is based on an assumption that ribosomal dysregulation may affect the lifespan via modulating sentinel organs. In addition to the mammalian aging model, the present study evaluated a gut model of C. elegans since it is an evolutionarily conserved responder to both endogenous and environmental stressors. In particular, the stress signaling response from the ribosome was associated with gut distress during the aging process. Our results provide new insights into the roles of ribosomes in the regulation of human longevity and susceptibility to gut-associated chronic diseases.

Human transcriptome dataset analysis
The Genotype-Tissue Expression (GTEx, V8) dataset was used for aging-associated gene expression analysis (17,382 sample entities of 54 tissues from 948 donors). RNA-seq raw counts and normalized TPM matrices from the normal transverse colonic tissues were quantified for comparison among different age groups.

Animal ethics statement
The experimental protocols followed in this study were reviewed and approved by the Animal Ethics Committee of Pusan National University (PNU)-IACUC (PNU-2015-0786 and PNU-2013-0291). The animal study was performed in accordance with the guidelines for animal experiments issued by PNU-IACUC.

Murine insult experiments: aging rat model
Sprague-Dawley rats (aged 6 and 19 months; N = 6 per group) were purchased from Samtako Bio Korea (Osan, Korea) and acclimated to the animal care facility for 7 days before beginning the experiments. Based on a previous report [35], animals were housed in an air-conditioned environment under a 12 h light/dark cycle and were given ad libitum access to standard rodent chow (Samtako) and water. Food intake and body weight were monitored every 5-8 days. The rats were euthanized, tissues collected, and perfusion was immediately fixed. Formalin-fixed and paraffin-embedded sections (thickness, 5 μm) were prepared from the small intestine. Slides were stored at room temperature until analysis.

Murine insult experiments: mouse model
Male C57BL/6 young (6 weeks) and old (19 months) mice from the Korean Distributor of Taconic Biosciences (DBL, Eumseong, Korea) were exposed to 25 mg/kg of DON for 12 h by oral gavage and then killed. Male C57BL/6 mice (8 weeks old) were caged in groups of 3-4 mice, and the cages were changed weekly. Briefly, mice were inoculated intragastrically with streptomycin sulfate (single dose, 10 mg per mouse) to selectively eliminate intestinal facultative anaerobic bacteria. After 22 h, the mice were inoculated with phosphate-buffered saline (PBS) or p38 inhibitor (SB203580, 10 mg/kg) by intraperitoneal (I.P.) injection. After a further 2 h, the mice were inoculated intragastrically with either PBS or DON (25 mg/kg). After 24 h, the mice were inoculated intragastrically with either sterile LB broth or EPEC bacteria (1 × 10 9 CFU). The mice were killed on the day 4 after bacterial inoculation, and their tissues were fixed in Carnoy's solution.

Histological assessment
Hematoxylin and eosin staining was performed using standard procedures described in our previous reports [36,37]. Scoring of disease severity was based on the following four parameters. Inflammatory cell infiltration was scored as follows: 0, rare inflammatory cells in the lamina propria; 1, increased numbers of granulocytes in the lamina propria; 2, confluence of inflammatory cells extending into the submucosa; and 3, transmural extension of the inflammatory infiltrate. Crypt damage was scored as follows: 0, intact crypts; 1, loss of the basal half; 2, entire crypt loss; and 3, confluent erosion. Ulceration was scored as follows: 0, absence of ulcer; 1, one or two foci of ulcerations; 2, three or four foci of ulcerations; and 3, confluent or extensive ulceration. Edema was scored as follows: 0, absence of edema; 1, mild; 2, moderate; and 3, severe.

Immunohistochemistry
Methods are based on our previous reports [37][38][39]. Before immunostaining, formalin-fixed paraffin-embedded tissues from mouse intestines were sectioned (5 μm), deparaffinized, and rehydrated. Tissue slides were heated in 10 mM sodium acetate (pH 6.0) for 5 min at 121 • C for antigen retrieval and were bathed in a 0.3% H 2 O 2 -PBS solution for 15 min at room temperature in the dark to quench endogenous peroxidase. After the samples were washed with Tris-HCl-Tween 0.5%, tissue sections (5 μm) were blocked with 5% fetal bovine serum in PBS, incubated in a 1:250 dilution of primary antibody overnight at 4 • C, and repeatedly washed using PBS. The primary antibodies used were anti-p-p38 (1:1000 dilution; Santa Cruz Biotechnology, Santa Cruz, CA) and anti-lysozyme (1:250 dilution, Biobyt, Cambridge, UK). The samples were incubated with secondary horseradish peroxidase-conjugated anti-mouse or anti-rabbit IgG antibody (1:250 dilution; Enzo Life Sciences, Inc. Farmingdale, NY, USA) for 2 h at room temperature and were subjected to repeated washing with PBS. Bound antibodies were identified by incubation for 10 min with freshly prepared substrate buffer (0.05% diaminobenzidine [DAB; Sigma-Aldrich Chemical Co.] and 0.015% H 2 O 2 in PBS). After a final wash in PBS and dH 2 O, the slides were counterstained with Mayer's hematoxylin for 5 min and dehydrated in a graded alcohol series (50%, 70%, 80%, 90%, 95%, 100%, and 100%). The sections were then examined at various magnifications using an Axio Imager (Carl Zeiss MicroImaging, GmbH, Oberkochen, Germany). To analyze the proportion of positively stained cells in the tissue, at least four representative areas were measured by computer-assisted analysis using HistoQuest image analysis software version 4.0 (TissueGnostics, Vienna, Austria). The pixels of images viewed in this software are converted to grayscale and assigned an arbitrary number relating to staining intensity. HistoQuest image analysis software differentiates hematoxylin-positive and diaminobenzidine (DAB)-positive cells or areas that are recorded as events (cells/nuclei) or area of staining. The results allow determination of "how many cells express how much of a given marker". Values were optimized by counting the numbers of DAB-positive events and hematoxylin-positive events. The graphs statistically compare the values among groups.

Alcian blue (AB) staining of murine tissues
Methods are based on our previous reports [37,38]. For AB staining, sections were deparaffinized in xylene, rehydrated in ethanol, and incubated in dH 2 O for 5 min. AB 8GX (Biosesang Company) was applied to sections for 30 min at room temperature, followed by a 2 min wash under running tap water, and counterstaining with Nuclear Fast Red or hematoxylin. Stained sections were rapidly dehydrated in two changes of 95% ethanol, followed by two changes of absolute alcohol, cleared in xylene, and mounted in a synthetic mountant (Thermo Fisher Scientific, Seoul, Korea).

Gram staining of murine tissues
Gram staining was performed as follows: tissue sections were deparaffinized in xylene, rehydrated in ethanol, incubated in dH 2 O for 5 min, stained for 1 min using crystal violet, and then washed under tap water [37,39]. Slides were then stained with Gram's iodine for 1 min, rinsed under tap water, dipped in 95% ethyl alcohol for 5-10 s, and rinsed again under tap water. The slides were stained again for 1-2 min in safranin and rinsed under tap water. Stained sections were rapidly dehydrated in two changes of 95% ethanol, followed by two changes of absolute alcohol, cleared in xylene, and mounted in a synthetic mountant (Thermo Fisher Scientific, Korea). The safranin stains decolorized gram-negative cells red/pink, while the gram-positive bacteria remained blue.

Gut epithelial F-actin staining
Paraffin sections were treated in xylene and rehydrated in an ethanol and water gradient. To detect F-actin, sections were incubated with tetramethyl rhodamine B isothiocyanate-conjugated phalloidin (0.5 μg/ ml, Sigma-Aldrich, USA) for 2 h in the dark [37]. Sections were then washed and counterstained with DAPI to detect nucleic acid.

Fluorometric assay of FITC-conjugated dextran in vivo
Methods are based on our previous reports [37,38]. Intestinal permeability was evaluated in different groups of mice using orally administered permeability marker FITC-conjugated dextran dissolved in water (4 kDa, 50 mg/ml; Sigma-Aldrich) as described in our previous report [37]. The blood serum was collected 3 h later, and the serum concentration of FITC-conjugated dextran was determined using a plate reader (SpectraMAX M2e, Molecular Devices, Sunnyvale, CA, USA). The sample emission spectrum (535 nm) was recorded after excitation at 490 nm. The concentration of FITC-conjugated dextran in the serum was determined using a FITC-dextran standard curve.

Barrier integrity assessment in the intestinal epithelial cell monolayer
For the measurement of TEER based on the previous study [40], intestinal porcine epithelial cell line-1 (IPEC-1; Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany) was seeded at a density of 4 × 10 5 per well in 24-well transwell filters with 0.4 μm pores (Becton-Dickinson Labware, Franklin Lakes, NJ, USA). The cells reached confluence within 2 days. For cell differentiation, complete DMEM/F-12 media containing 100 nM dexamethasone was used and changed every other day until complete differentiation [41]. At the end of the differentiation process (on the 10th day after the addition of dexamethasone), cells were treated with 1000 ng/ml DON. TEER was measured every 12 h using an EVOM2 epithelial voltohmmeter (World Precision Instruments, Sarasota, FL, USA). Experimental TEER values were expressed as Ω cm 2 . All measurements were performed in triplicate. For the paracellular tracer flux assay based on the previous report [40], differentiated IPEC-1 in 0.4 μm pore inserts (generated as described above) were treated with 1000 ng/ml DON for 48 h. Next, 4 kDa FITC-dextran (Sigma-Aldrich Chemical Company) was dissolved in cell culture medium and added to the apical compartment with enterocytes at a final concentration of 2.2 mg/ml. After 1 h of incubation, the amount of fluorescence in the basolateral compartment was measured using a Victor3 fluorometer (PerkinElmer, Waltham, MA, USA). The excitation and emission wavelengths were 490 and 535 nm, respectively. Data presented are representative of three independent experiments [42].

Worm culture conditions
Methods for maintenance of C. elegans are based on our previous reports [37,38] [37,38]. C. elegans were synchronized with a mixture of 500 μl 5 N NaOH, 1 ml 5% solution of sodium hypochlorite (Yohanclorox, Seoul, Korea), and 3.5 ml autoclaved dH 2 O. Synchronized eggs were seeded on NGM agar plates for growth, and worms at the L4 stage were seeded on new NGM agar plates spread with chemicals of interest (see below) and E. coli OP50 (OD 600 = 0.6). To prepare C. elegans media for experimental treatments, chemicals were added to E. coli OP50 culture media, which was spread on the culture dishes. For lifespan assays, 30 synchronized worms were grown on NGM agar plates spread with E. coli OP50 with chemicals at 24 • C. The surviving worms that moved in response to touch with a platinum wire were scored each day. To prevent progeny production, worms were transferred to new NGM plates every 2 days.

Bacterial colonization assay in the worms
To prepare for bacterial colonization assays, the worms were agesynchronized by bleaching, and embryos were incubated at 24 • C on NGM plates containing E. coli OP50 until the L4 stage, as previous described [37,38]. After exposure to DON for 48 h, 10 worms were selected and placed into a 2 ml tube containing 25 mM levamisole to induce paralysis and inhibit pharyngeal pumping. Then, LM buffer (25 mM levamisole in M9 buffer) containing 100 μg/ml gentamicin was added, followed by incubation for 45 min to remove the surface bacteria. The worms were then washed three times in LM buffer to remove bacteria and antibiotics. The washed worms were incubated with 1% Triton X-100 and mechanically disrupted using a motor pestle for 3 min. Worm lysates were diluted in M9 buffer, spread on Luria-Bertani (LB) agar containing 50 μg/ml streptomycin, and incubated overnight at 37 • C. Colonies were quantified, and the number of bacteria per nematode was calculated. For the fluorescence visualization, E. coli OP50 were transformed with the GFP-UV vector (which is resistant to Ampicillin, 100 μg/ml) for bacterial colonization assays [37,38]. The intestinal GFP-UV-OP50 fluorescence of worms was examined by fluorescence microscopy using an Axio Imager M2 (ZEISS, Gottingen, Germany). The Multi Gauge V. 3.0 (Fusifilm Life Science, USA) program was used to measure intestinal GFP-OP50, bacterial distribution, and width and length of the intestines.

Intestinal barrier function assay (Smurf assay)
Methods for C. elegans are based on our previous reports [37,38]. After 24 h DON treatment, worms from the NGM agar plates were cultured for 3 h in the liquid media of UV-killed E. coli OP50 that was mixed with blue food dye (TCI, FD&C Blue #1, B0790, Portland, OR, USA; 5.0% wt/vol in NGM liquid solution). The worms were then washed in M9 buffer until the blue color was no longer visible and were anesthetized in M9 buffer containing 25 mM levamisole. The worms were then analyzed on a slide to determine the presence of blue food dye in the body cavity by using an Eclipse Ts2R microscope (Nikon, Tokyo, Japan) under 20 × magnification. Data were analyzed using GraphPad Prism software.

DAPI staining of worms
The worms washed in M9 buffer were mounted on slides. After wiping away surplus liquid using Whatman paper, 95% ethanol was added and allowed to dry. This procedure was repeated four times. The fixed worms were then added to fluorescent mounting medium containing DAPI solution (IHC World, Woodstock, MD, USA).

Stool sample collection and DNA extraction
After 24 h of DON administration, stool samples (5-6 pieces) (100 mg) of fecal samples were collected before animal sacrifice. The collected stool samples were stored at − 150 • C prior to DNA extraction. Microbial DNA was extracted and purified from 100 mg of the fecal sample using Exgene Stool DNA mini kit (GeneALL, Seoul, Korea) according to the manufacturer's instructions. The extracted DNA was quantified using Qubit fluorometer and a high-sensitivity dsDNA reagent kit (Invitrogen, Carlsbad, CA, USA).

Amplification of the 16S rRNA genes and library preparation
A total DNA samples were pooled as treatment groups for a 16S metagenomic library preparation. The V4 region of the 16S rRNA genes was PCR-amplified with a primer set (533F, 5 ′ -GTGCCAGCMGCCGCGGTAA-3'; 806R, 5 ′ -GACTACHVGGGTWTCTAAT-3 ′ ) and Illumina sequencing adaptors (Illumina Inc.) using the KAPA HiFi HotStart Ready Mix (KAPA Biosystems, Wilmington, WA, USA) under the following cycling conditions: initial denaturation 95 • C for 3 min, followed by 25 cycles of denaturation at 95 • C for 30 s, annealing at 55 • C for 30 s, extension at 72 • C for 30 s, and a final extension at 72 • C for 5 min. After amplicon purification using AMPure® XP beads (Agencourt Biosciences, Beverly, MA), PCR products were validated for library size using a BioAnalyzer. Then, the PCR amplicon was subjected to indexing PCR using the Nextera XT Index Kit (Illumina, Inc.). The PCR cycling conditions were as follows: 95 • C for 3 min, followed by 8 cycles of denaturation at 95 • C for 30 s, annealing at 55 • C for 30 s, extension at 72 • C for 30 s, and a final extension at 72 • C for 5 min. The indexed PCR amplicons were purified using AMPure® XP beads, verified for size using a BioAnalyzer, and quantified using the Qubit fluorometer. The quantified amplicons were diluted to 1 nM and pooled together for sequencing on an Illumina iSeq platform (Illumina, Inc.), targeting 2 × 150 bp paired-end sequence reads.

Microbiome data processing
All sequences were quality-filtered and the primers were trimmed using Trimmomatic (v0.39) [43] with the following parameters: LEADING:3 TRAILING:3 MINLEN:36 SLIDINGWINDOW:4:15). The read pairs passing the quality filter were further analyzed using the Quantitative Insights Into Microbial Ecology 2 (QIIME2, v2020.2) pipeline [44]. Using the DADA2 algorithm [45], the 34 bases of the reverse reads were truncated for quality improvement. The read pairs were then joined, denoised, and dereplicated, and chimeras were removed. The detailed data were used to call ASVs. The 16S rRNA representative sequences were assigned to taxonomic groups using the QIIME2 naive Bayes classifier trained on 99% operational taxonomic units (OTUs) and the primer region from the SILVA rRNA database (v132) [46]. The taxonomic classification was visualized using the QIIME2 taxon barplot plugin. The 16S rRNA representative sequences were subjected to masked multisequence alignment using MAFFT [47,48]. A phylogenetic tree was constructed using FastTree [49]. A heatmap, depicting the percentage abundance of OTUs, the relative abundance of which was in the top 30 in any sample, was generated using the R package, QIIME2R (v0.99.22). A phylogenetic tree based on the abundant OTUs was constructed and visualized using the neighbor-joining algorithm with Jukes-Cantor correction using MEGA X [50]. Additionally, we used the CLcommunity™ software for analysis.

Statistics and reproducibility
Statistical analyses were performed using GraphPad Prism v. 8.4.2 (La Jolla, CA, USA). For comparative analysis of two groups of data, Student's t-test was performed. For comparative analysis of multiple groups, data were subjected to analysis of variance (ANOVA) with   Results are shown as a plot with Tukey whiskers. Different letters over each box represent significant differences between groups (p < 0.05). C-D. Representative images of N2, Sek-1, Pmk-1 mutant animals after 3 h of staining with blue food dye. Animals were exposed to 24 h of treatment with the vehicle or 500 ng/ml DON (C). Black arrows indicate areas where blue food dye has leaked from the gut lumen into the body cavity, giving rise to the Smurf phenotype.  Results are shown as a plot with Tukey whiskers. Different letters over each box represent significant differences between groups (p < 0.05). C. FITC-dextran concentration in blood samples from C57BL/6 mice exposed to vehicle or chemical ribosomal stressor (25 mg/kg DON) for 3 h (**p < 0.01, N = 6). Results are shown as a plot with Tukey whiskers. Asterisks represent a significant difference between groups (p < 0.05). D-F. Young (6 weeks) and old (19 months) mice were exposed to vehicle or chemical ribosomal stressor (25 mg/kg DON) for 12 h (N = 20). Small intestines were isolated for the analysis of goblet cells and mucin production, which was visualized by staining with alcian blue ( × 200; scale bar(s), 100 μm) (D), and expression levels were quantified using Histoquest analysis software (E). Additionally, the numbers of the goblet cells per villus were compared in each group (F). Results are shown as a plot with Tukey whiskers. Different letters over each box represent significant differences between groups (p < 0.05). G-H. Epithelial permeability of differentiated IPEC-1 cells exposed to each combination of chemicals at different time points using TEER (G) and FITC-dextran (H). Different letters over each bar represent significant differences between groups (p < 0.05). Data represent three independent experiments. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) Newman-Keuls method as a post hoc ANOVA assessment. For two gene correlation coefficient (R) determination in IBD-based datasets, Pearson's correlation analysis was performed. All in vitro evaluations are representative of two or three independent experiments. Details of the number of biological replicates and the assays are given in each figure legends.

Effect of ribosomal stress on the lifespan of C. elegans under the ribosomal stress
Ribosomal stress responses were assessed in human aging using the transcriptome dataset of nondiseased tissue from the Genotype-Tissue Expression (GTEx) project. We assessed key sentinel molecules in response to ribosomal dysfunction in the gut. In particular, protein kinase R (PKR) and leucine-zipper and sterile alpha motif containing kinase (ZAK) are crucial upstream sentinel molecules during ribosomal stress responses [19,51]. Levels of PKR and ZAK transcript expression in elderly subjects were significantly higher than the young subjects ( Fig. 1A and B), indicating the ribosomal stress increases with the aging process. Simulative assessment of the aging-associated effects of ribosomal stress was evaluated using C. elegans rather than mammalian models, since the average lifespan of a wild-type worm is approximately 3 weeks. In terms of lifespan, the survival of wild-type worms (N2) decreased with exposure to a representative chemical ribosomal stressor (deoxynivalenol, DON) in a dose-dependent manner (Fig. 1C). According to the extensive survey in 7049 cereal samples sourced in North and South Americas, Europe, and Asia during 2009-2011, 59% of samples were contaminated with DON, and an average contamination level was 1 mg DON/kg grain [52]. Therefore, in the present models, we used 50-1000 ng/ml DON which corresponds to mammalian exposure levels (0.05-1.0 mg DON/kg diet), which is supposed to be realistic within the average contamination level [40]. Moreover, epithelial exposure to 50-1000 ng/ml DON increases the proinflammatory cytokine expression in a dose-dependent manner despite ribosomal stress-induced cell cycle arrest [3,53]. In the present study, most of mucosal exposure was performed at 20% cell growth-inhibitory dose of the ribotoxic stressor (ID 20 , 500 ng/ml DON). In mammalian cells, p38 MAPK signaling is a central signaling mediator of cellular stress responses to ribosomal dysfunction [54][55][56]. On an assumption that stress-responsive p38 MAPK signaling can be involved in the aging process, it was assessed in the nematode model. The involvement of the p38-activated pathway in the regulation of the lifespan was assessed in C. elegans under the ribotoxic stress. Sek-1 encodes a MAPK kinase (MAPKK) that phosphorylates the p38 MAPK Pmk-1 in C. elegans. Sek-1 and Pmk-1 mutant animals displayed shortened lifespan compared to the wild-type (N2) in response to the chemical ribosomal insult ( Fig. 1D and E, respectively). Moreover, genetic mutation of Pmk-1 downstream signaling transcription factors including ATF-7 and SKN-1 also notably reduced the lifespan of the nematode ( Fig. 1F and G, respectively). Taken together, these results indicate that the p38 MAPK signaling positively influences the lifespan of worms in both normal and ribotoxic stress states.

Disruption of gut epithelial and mucosal barriers in intoxicated hosts
Since the aging process is closely associated with deterioration of the gastrointestinal tract as mentioned in the introduction, we analyzed expression levels of human intestinal integrity biomarkers using the transcriptome dataset of nondiseased tissue from GTEx project. It was initially hypothesized that the host ribosomal stress is elevated with aging (Fig. 1A), which will influence the gut barrier integrity during the stress. We specifically evaluated correlation of the ribosomal stress markers (PKR and ZAK) and levels of components of the mucosal and epithelial layer, including mucins, antimicrobial peptides (AMPs), and tight junction-associated molecules. In particular, among the components, the most of mucins are negatively associated PKR and ZAK ( Fig. 2A). In addition, the ribosomal stress marker levels displayed high correlation with expressions of the main intestinal claudins (CLDN3, CLDN7, CLDN8, and CLDN15), tight junction protein 3 (TJP3), and occludin (OCLN). Of note, expression of two mucins (MUC3A and MUC4) was assessed in different age groups. Compared to the levels in young age group (aged 20-29 years), expression of MUC3A and MUC4 tended to decrease with age, which was prominent in elderly groups (aged 60-79 years) ( Fig. 2B and C, respectively). Moreover, expressions of barrier-associated molecules (MUC3A and CLDN3) were downregulated in high PKR or high ZAK subjects (Fig. 2C-F), verifying the inverse correlations between ribosomal stress and barrier integrity. From human dataset-based evaluation, it was hypothesized that gut stress-induced barrier disruption may be a crucial cause of reduced longevity in aged organisms. The mechanism of ribosomal stressassociated regulation of the lifespan was investigated further in nematode guts. Morphologically, the ribosomal insult caused a reduction in the number of intestinal nuclei in the gut luminal lining (Fig. 3A and B). Furthermore, Sek-1 and Pmk-1 mutants exhibited a more reduction in the number of intestinal nuclei than that in wild-type animals, which was gradually aggravated with time (day 5 and day 12) (Fig. 3A and B). Barrier integrity was tested using a non-absorbable blue food dye and was microscopically visualized. While the majority of staining was observed within the intact gut lumen, ribosomal stress led to partial spreading of the dye into their body cavity ( Fig. 3C and D). In contrast, Sek-1 and Pmk-1 mutants experienced severe leakage of the dye into the body cavity in response to the ribosomal stress as the exposure time increases. Moreover, Sek-1 mutants displayed an extensive spread of the luminal contents in the body cavity via severe loss of the gut epithelial barrier integrity in response to DON at day 12. The relative permeability of the gut barrier was monitored by staining the fat under the intestinal epithelial layer using Nile red as a lipophilic fluorescent dye after epithelial barrier disruption (Oil red O was used to counterstain major lipid storage region in the nematodes, Supplementary Figs. 1A-1B). After 24 h exposure to chemical ribosomal stressor, permeability (relative ratio of Nile red/Oil red O) was confirmed, and there was a notable increase in gut permeability in sek-1 deficient animals in response to the ribosomal insult. Moreover, cross-sectional observation of the nematode gut barrier under an electron microscope demonstrated that Sek-1 deficiency led to disruption of the gut epithelial layer in response to the ribosomal insult, although there were rare histological injuries in the horizontal gut epithelial layer of wild-type animals in response to the ribosomal insult in the early age (day 2) (Supplementary Fig. 1C). All of these evidences support the barrier-protective action of Sek-1/Pmk-1linked signaling in response to the ribosomal insult.
Consistent with the results in the nematode, comparative analysis in the murine model showed a similar pattern of gut barrier regulation. Aging-associated reduction of Sek-1-linked signaling was confirmed in the mouse intestine. In addition to the basal levels of p38 phosphorylation, levels of ribosomal stress-activated p38 MAPK were also attenuated in the guts of old mice (Fig. 4A and B). Functionally, agingassociated barrier responses were evaluated by measuring serum levels of luminally applied fluorescein isothiocyanate [FITC]-dextran in the mice (Fig. 4C). As in the nematode model, the ribosomal insult increased serum FITC-dextran in the mouse gut. Stress-induced barrier disruption was notably aggravated in the elderly subjects compared to the young group. In addition to the epithelial barrier, the mucosal layer and mucin secretions were impaired by the ribosomal insult in mouse gut lumen (Fig. 4D). Compared to young mouse guts, old mouse intestines exhibited similar levels of mucus secretion and goblet cells; however, mucus secretion and number of goblet cells in response to ribosomal stress were severely attenuated in the old animal group, indicating age-associated aggravation of barrier responses to gut insult ( Fig. 4E and F). We further mechanistically evaluated the roles of the Sek-1/p38 MAPK signaling pathway in maintaining gut barrier integrity in response to mucosal insult using a porcine enterocyte monolayer  3) and results are shown as mean values ± SD. Asterisks (*) indicate significant differences between groups (*p < 0.05, ***p < 0.001). D. mRNA levels of T24B8.5, lys-7, and lys-8 worms exposed to vehicle or chemical ribosomal stressor (500 ng/ml of DON) for 48 h determined using qPCR. Data represent three independent experiments. Different letters represent significant differences between groups (p < 0.05). E. Longitudinal distribution of OP50 in the guts of nematodes exposed to vehicle or chemical ribosomal stressor (500 ng/ml of DON) for 48 h. Data represent three independent experiments.
(caption on next page) culture. The ribosomal insult led to a reduction in transepithelial electrical resistance (TEER) in monolayer cultures of porcine enterocytes (IPEC-1), which was exacerbated by the blocking of the Sek-1/p38 MAPK signaling pathway (Fig. 4G). Moreover, measurements of intestinal monolayer permeability using FITC-dextran confirmed the deterioration of barrier integrity via the inhibition of p38 MAPK signaling in response to the ribosomal insult (Fig. 4H). Together, these data demonstrate the pivotal function of epithelial p38 MAPK in preventing gut barrier disruption in response to ribosomal insult. The old animals displayed low levels of p38 signaling, thus indicating their high sensitivities to ribosomal stress-induced gut barrier disruption.

Ribosomal insult increases bacterial exposure in the nematode gut
The maintenance of gut barrier integrity is crucial for the control of microbial colonization during aging. Distribution of gut bacteria was monitored using GFP-labeled OP50. GFP-labeled microbes were quantified in the gut lining, from the mouth to the anus (Fig. 5A-B). At the early age of day 2, gut bacterial levels were too marginal to detect. However, bacterial colonization increased with time of the aging, which was notable under the ribosomal stress at day 12. Moreover, Sek-1 and Pmk-1 mutants displayed significantly increased levels of bacterial colonization. In particular, Sek-1 and Pmk-1 mutants demonstrated increased transverse migration from the gut lumen to the inner body cavity in response to ribosomal insult relative to that in wild-type nematodes. Moreover, the quantitation of the gut bacteria verified the microscopic observation of bacterial colonization and translocation (Fig. 5C). The ribosomal stress increased the number of intestinal E. coli. More bacteria were detected in Sek-1 and Pmk-1 mutants than in the wild type animals, indicating the preventive roles of Sek-1/p38 MAPKassociated signaling against bacterial access and colonization in this nematode gut model.
In response to bacterial access, C. elegans has evolved its defense against microbial exposure by producing lysozymes in addition to the epithelial barrier junction. In response to ribosomal stress, the animals suppress the production of lysozymes, other than lysozyme 7; however, Sek-1 mutants displayed attenuated induction of lysozymes, indicating the positive regulation of lysozyme expression by Sek-1 signaling (Fig. 5D). Moreover, GFP-labeled OP50 was quantified in the three segments of the gut lining, from the mouth to the anus during longitudinal observation. Lysozyme-mutant animals were more susceptible to longitudinal translocation of the gut bacteria at the distal section of the gut in response to the ribosomal insult (Fig. 5E). Taken together, Sek-1 signaling was found to be important in microbial regulation of the nematode gut in response to the ribosomal stress via lysozyme production and enhanced integrity of the mucosa and epithelial lining.

Disruption of the murine intestinal barrier allows greater microbial access
Consistent with the results from the nematode gut model, ribosomal insult increased susceptibility to luminal bacterial translocation in the murine model. Enteropathogenic E. coli (EPEC) is a human enteric pathogen that attaches to the surface of intestinal epithelial cells and causes watery diarrhea; however, in mice, EPEC infections result in relatively mild symptoms, such as histological changes (e.g., increased numbers of lamina propria neutrophils) without diarrhea [57]. In the present study, EPEC infection did not cause diarrhea or intestinal shortening in the murine model (Fig. 6A); however, EPEC infection shortened colon length in ribosome-insulted animals, which was much more severe when p38 MAPK was inhibited. Moreover, inhibition of p38 MAPK increased mouse mortality, implicating p38 MAPK-dependent resistance to EPEC infection (Fig. 6B). Similarly, the length of the intestinal villi was shortened in response to EPEC infection, and this phenomenon was particularly prominent in ribosome-insulted hosts in a p38 MAPK-associated manner (Supplementary Fig. 1D). In terms of pathological scoring, the ribosomal insult aggravated infection-linked disease severity in the gut. Moreover, p38 inhibition in the EPEC-infected gut with pre-exposure to the ribosomal stress resulted in severe epithelial loss, ulceration, and inflammatory lesions ( Fig. 6C and  D). Intestinal epithelial loss was further confirmed by the measurement of claudin 3 expression as a representative junctional molecule in response to the ribosomal insult. Epithelial claudin 3 was similarly regulated in the murine infection model. The ribosomal insult reduced the expression of claudin 3, which was severely reduced in host guts where p38 MAPK was inhibited during EPEC infection (Fig. 6E). F-actin is strongly localized to the apical surface of the epithelium, while the ribosomal insult exacerbated the disruption of this marker of epithelial polarity in response to EPEC infection, triggering the spread of F-actin to basolateral regions (Fig. 6F). Furthermore, the inhibition of p38 MAPK led to a complete loss of polarity and even distribution of F-actin throughout the epithelial cells, indicating the regulatory role of p38-linked signaling in the maintenance of enterocyte polarity in response to the ribosomal insult.
In addition to influences on the epithelial barrier, the mucosal barrier was assessed by evaluating mucus secretion by the gut epithelia. EPEC infection led to a slight decrease in mucus secretion, which was exacerbated by the ribosomal stress (Fig. 7A). Of note, p38 inhibition completely depleted mucus production in the guts of mice infected with EPEC after ribosomal insult. Along with a reduction in mucin production, secretion of the antibacterial peptides such as lysozymes demonstrated similar patterns in the murine model. The ribosomal insult significantly downregulated lysozyme expression in the guts of mice infected with EPEC, while p38 MAPK positively regulated lysozyme secretion in response to EPEC infection (Fig. 7B). The insult disrupted the mucosal barriers (mucus and lysozyme secretion) between luminal bacteria and epithelia, which are crucial for defense against luminal bacteria. Barrier disruption led to increased accessibility of the gut bacteria (Fig. 7C) and EPEC (Fig. 7D) to the gut epithelial layer. Moreover, p38 inhibition in EPEC-infected guts displayed complete loss of the mucosal barrier and subsequent access of luminal commensal and pathogenic bacteria toward the epithelial layer under the ribosomal insult ( Fig. 7C and D), indicating a protective role of the p38 pathway against gut microbiota and enteric pathogens, consistent with findings in Fig. 6. Ribosomal insult exacerbates colitis and barrier disruption in a p38-dependent manner Mice pretreated with p38 inhibitor (P38I, SB203580, 10 mg/kg, I.P.) for 2 h were exposed to vehicle or chemical ribosomal stressor (25 mg/kg DON) via gavage for 24 h then were orally infected with sterile LB broth or EPEC (1 × 10 9 CFU) for 4 days (N = 13). A. After treatment, colon lengths were compared between groups and were quantified (the right graph). Results are shown as mean values ± SD. Different letters over each bar represent significant differences between groups (p < 0.05). B. Survival proportion of exposed mice. C. Representative histologic observations of hematoxylin and eosin (H&E)-stained small intestine sections by microscopy (left panels) and pathological severity (right graphs) ( × 200; scale bar(s), 100 μm). Different letters over each bar represent significant differences between groups (p < 0.05). D. Extent of ulcerated areas in H&E-stained small intestine samples. Results are shown as a plot with Tukey whiskers. Different letters over each box represent significant differences between groups (p < 0.05). E. Images of claudin 3 (green) expression in the small intestine and the relative expression (%) of claudin 3 in tissue samples counterstained with DAPI ( × 400; scale bar(s), 50 μm). The right graph: quantification of claudin-3 in tissue samples. Results are shown as a plot with Tukey whiskers. Different letters over each box represent significant differences between groups (p < 0.05). F. F-actin (red) staining and DAPI nuclear staining (blue). White arrows indicate the altered localization of F-actin from apical distribution (left panel) and relative spreading of apical F-actin (%) (the right graph) ( × 400; scale bar (s), 50 μm). Results are shown as a plot with Tukey whiskers. Different letters over each box represent significant differences between groups (p < 0.05). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) the nematode model (Fig. 5). Taken together, our data indicate that ribosomal insult disrupts the integrity of both the epithelial and mucosal barrier in a p38 MAPK-dependent manner, which may account for the increased invasion of commensal and pathogenic bacteria in the murine model.

Ribosomal stress-responsive microbiota predicts the mucin degradation in the aged animals
In addition to stress-induced barrier disruption during the aging, we evaluated the effects of the chemical ribosomal stressor (DON) on the microbial community in the aged subjects. Since DON specifically binds to the eukaryotic ribosomes, the potent effect of DON on the bacterial community would be due to alterations in host pathophysiological states rather than a direct impact on the microbes. The alpha diversity based on the Shannon metrics showed that the microbial communities under the ribosomal stress had a lower degree of diversity than those in the aged control mice (Supplementary Fig. 2A). Moreover, the ribosomal insult caused pronounced changes in gut microbiota composition, such as an increased proportion of Phylum Bacteroidota with a decrease in Phylum Firmicutes (Supplementary Fig. 2B). Furthermore, the decrease in Firmicutes was due to reduction in Class Clostridia population whereas the increase in Phylum Bacteroidota was linked to the reduced population of bacteroidia in response to chemical ribosomal insult ( Supplementary Fig. 2C). Closer observation of high abundant genus or species demonstrated that the abundance of Muribaculum and Duncaniella dubosii were notably elevated by the ribosomal stress (Fig. 8A). In terms of mucosal barrier disruption, members of the underexplored family Muribaculaceae were recently revealed as major mucin monosaccharide foragers [58]. In the present study, the concomitant increase in Muribaculaceae members such as Muribaculum and Duncaniella dubosii predicts the disruption of the mucosal layer over the epithelial lining in response to the ribosomal stress and altered bacterial community. Furthermore, the metagenomic prediction based on the 16S rRNA profile using PICRUSt2 demonstrated significant elevations in genes encoding mucin degradation-related enzymes in response to ribosomal stress in the aged mice (Fig. 8B) 25), and alpha-N-acetylglucosaminidase (EC:3.2.1.50) were significantly enhanced by the chemical ribosomal stress in the aged mice. Taken together, the abundances in mucin metabolism-linked bacteria and enzymes were concomitantly increased with the mucosal barrier disruption by the ribosomal stress with aging.

Discussion
In response to internal or external insults, the ribosomes are crucial in managing the cellular integrity. Since levels of biomarkers for the ribosomal stress and dysfunction are elevated with aging in humans, we simulated the actions of the ribosomal dysfunction in the aging process using a most frequently-exposed ribosome-targeting agent in human environment. DON is the most frequently occurring ribotoxic stressor primarily produced by Fusarium species [59,60]. The molds colonize more than 60-70% of global agricultural commodities, including a variety of cereal-based foodstuffs and animal feed [4,5]. In addition to contaminating crops, DON is released into surface soil water and drainage systems and is a micropollutant in soil and aqueous environments [6][7][8]. The aged hosts exhibit greater susceptibility to DON-induced disruption of the epithelial and mucosal barriers of the gut than that by young animals, which was concomitant with attenuation of Sek-1/p38-associated signaling during aging process. Since Sek-1 is positively involved in the maintenance of junctional molecules and the expression of antimicrobial lysozymes, its signaling attenuation in the aged subjects may account for their increased susceptibility to barrier disruption and subsequent microbial access. Moreover, the abundances in mucosa-foraging bacteria and genes for mucin metabolism-linked enzymes were elevated with the mucosal barrier disruption in the ribosome-insulted aged subjects (Fig. 8C). For longevity, it is crucial to maintain barrier-associated defenses against microbial access. A weakened gut lining in aged hosts may allow exogenous or indigenous microbes to translocate through the barrier and evoke inflammatory responses, leading to sepsis and mortality, although the microbes themselves are hardly pathogenic. During the aging process, ribosomal stress-activated Sek-1/p38 MAPK-associated signals are considered beneficial for the defense against mucosal access and in counteracting gastrointestinal insult, including the barrier disruption. These endogenous defense signals diminish during aging and may exacerbate gut toxicity and disease susceptibility with dysbiotic bacterial community in elderly hosts. We have no direct evidence that the ribosomal insult directly reduces human life expectancy; however, the ribosomal stress-associated molecular features are elevated with aging process, which is closely associated with increased disruption of gut barrier integrity. Deleterious effects of aging-associated ribosomal insult on gut barrier integrity may contribute to increased susceptibility to life-threatening microbial and inflammatory risks in a Sek-1/p38-inversed way, which function similarly in both worm and mouse models. Considering molecular and cellular pathways, there is a notably high level of conservation between C. elegans and mammals including humans. Among the available 18,452 C. elegans protein sequences, at least 83% (15,344 sequences) of worm proteome has human homologous genes [61]. Moreover, worms present evolutionary conserved signaling pathways including the Ras, the Wnt, the Smad, and the Notch pathway as well as MAPK-associated signals, which are involved in complex human diseases or biological processes such as aging. Toxicological consistency between worms and mammals in the present study suggests a usefulness of the worm exposure model to elucidate the causal effects of environmental and human health risk factors in a one health perspective (Fig. 8C).
Among aging-associated molecular mechanisms, alterations of the translation machinery of the ribosomes can be a central module of the protein homeostasis during the aging process [62]. Excessive protein biosynthesis can lead to accumulation of erroneous malfunctional proteins which are potently toxic to cellar integrity during aging. The disturbance in protein homeostasis can be minimized by global translational arrest via depletion of components of the translational Fig. 7. Ribosomal insult exacerbates EPEC infection via gut barrier disruption in a p38-dependent manner Mice pretreated with p38 inhibitor (P38I, SB203580, 10 mg/kg, I.P.) for 2 h were exposed to vehicle or chemical ribosomal stressor (25 mg/kg DON) via gavage for 24 h then orally infected with sterile LB broth or EPEC (1 × 10 9 CFU) for 4 days (N = 13). A. Lysozyme expression in the small intestine with hematoxylin counterstaining ( × 200; scale bar(s), 100 μm). The relative lysozyme density was measured using Histoquest analysis software. Relative quantitation is shown as a plot with Tukey whiskers. Different letters over each box represent significant differences between groups (p < 0.05). B. Mucin secretion in small intestines (alcian blue, positive) ( × 200; scale bar(s), 100 μm). Right panel: mucus indices for images to the left. Relative quantitation is shown as a plot with Tukey whiskers. Different letters over each box represent significant differences between groups (p < 0.05). C. Gram staining to determine the ability of total bacteria to access the epithelial barrier ( × 100; scale bar(s), 100 μm). Relative quantitation of the mucosal thickness is shown as a plot with Tukey whiskers (the right graph). Different letters over each box represent significant differences between groups (p < 0.05). D. Measurement of GFP-labeled EPEC in the mucus to determine its ability to access gut epithelia, counterstained with DAPI ( × 200; scale bar(s), 100 μm). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) machinery [63]. Therefore, longevity can be associated with the attenuation of the global protein synthesis. However, some proteins are needed to be upregulated for damage repair and cellular maintenance during aging process. This selective upregulation of the protective proteins can be facilitated by stress-responsive signaling modules during the aging process. In the present study, we focused on ribosomal stress-activated Sek-1/p38 signaling as the protective platform in the epithelial cell integrity. p38 MAPK is an evolutionarily conserved MAPK subfamily that has four isoforms, α, β, δ, and γ. It can act as a direct sentinel for various stress factors, including oxidative stress, inflammatory cytokines and growth factors, and organelle-associated stress [1,2,64]. In response to external and internal insults, cells undergo reprograming processes associated with proliferation, differentiation, and cell death in response to cues from tissues or circulating factors, including growth factors, hormones, and cytokines, which leads to the activation of MAPK [1]. In particular, when ribosomal insults are recognized by the ribosomal scaffold, p38 MAPK signal transduction pathways are activated in human and murine cells [1,2]. In the present study, Sek-1/p38 MAPK signaling protected both mammalian and C. elegans model animals against aging-associated gut damage, whereas Sek-1/p38 MAPK signaling-mediated benefits were attenuated by the aging process. Aging is associated with a decline in the activity of the conserved p38 MAPK signaling pathway, which weakens innate immunity against luminal bacteria [65]. Among the downstream transcription factors in C. elegans, activating transcription factor 7 (ATF7), an ortholog of mammalian ATF2/ATF7, mediates the regulation of genes involved in innate immunity, including C-type lectins, ShK toxins, and CUB-like genes [66,67]. In addition to its involvement in antimicrobial gene induction, Sek-1/p38 MAPK signaling contributes to prolonging animal lifespan through the regulation of oxidative stress responses [68]. Specifically, p38 MAPK mediates the activity of the oxidative stress-induced Cap-N-Collar transcription factor, SKN-1. In response to oxidative stress, enterocytes exhibit nuclear accumulation of the SKN-1 protein, leading to the induction of a phase II detoxification enzyme involved in the maintenance of gut barrier integrity [68]. The p38 MAPK-mediated regulation of oxidative stress has also been confirmed in a mammalian model [69]; therefore, diminished p38 MAPK signaling transduction in the aged host may aggravate oxidative injuries in the gut, while the production of antimicrobial mediators is reduced, allowing increased microbial invasion in the body.

Conclusion
Based on the genetic features of aging-associated ribosomal dysfunction in humans, we simulated the actions of the ribosomal stress in the aging process using DON, a ribosome-targeting xenobiotic agent. The aged hosts exhibit greater susceptibility to ribosomal stress-induced disruption of the epithelial and mucosal barriers of the gut than that by young subjects, which was concomitant with attenuation of Sek-1/p38associated signaling during the aging process. Since Sek-1/p38associated signaling is positively involved in the maintenance of junctional and antimicrobial molecules, aging-associated attenuation of the protective signaling accounts for the increased barrier distress and susceptibility to dysbiotic community including muco-foragers in the aged hosts. In order to provide translational interventions against ribosomal stress-linked gut aging, a systematic network of the chronic adverse outcome pathway needs to be carefully addressed in the future investigations.  Fig. 8. Ribosomal stress alters bacterial community in the aged mice A-B. Nineteen-month-old male mice were exposed to chemical ribosomal stressor (25 mg DON/kg body weight) via gavage for 12 h (N = 20), and stools were collected after the exposure. The fecal bacterial DNA was subjected to 16S rRNA analysis for the determination of the phylogenetic composition. A. The bacteria of each top 30 abundant taxa are listed along with the corresponding abundance in aged mice exposed to the vehicle or DON. B. Bacterial genes related to mucin degradation enzymes reconstructed from 16S rRNA profile with PICRUSt2. Data represent mean ± SD (N = 3) and results are shown as mean values ± SD. Asterisks (*) indicate significant differences between groups (*p < 0.05, **p < 0.01, ***p < 0.001). C. A variety of biological systems can be exposed to the ribosomal stressors including a most prevalent mycotoxin deoxynivalenol in the food chain on the planet. As a protective pathway in response to ribosomal stress, Sek-1/p38 MAPK signaling is activated to facilitate the induction of antimicrobial mediators and maintain barrier integrity. However, the aging process attenuates Sek-1/p38 signaling activation in the gut, thus, barrier defense is weakened due to reduced protective signaling, leading to increased insult by mucosa-degrading gut microbes with aging. Overall, the ribosomal insult-related gut deterioration is aggravated with aging, ultimately increasing infection-linked risk in a p38-dependent manner.